Selecting The Most Optimum Sentinel-2A Based Vegetation Index to Estimate the Leaf Area Index of Three Rice Cultivars

The estimation of leaf area index (LAI) becomes important as LAI is one of the parameters in analyzing the crop growth model. Crop growth has different characteristics and it’s strongly influenced by environmental conditions and factors. The growth tends to occur in a short period and covers a large area. Therefore, an approach to analyzing the pattern of changes in crop growth based on LAI spatially is needed. Remote sensing offers an effective and efficient approach to monitoring crop growth characteristics, which can be done in a time series with a wide area coverage by detecting and monitoring the physical characteristics of the crop. The most famous and commonly used parameters to estimate LAI are vegetation indices which are usually calculated based on the ratio of the red and NIR wavelength, known as a spectral signature. The objectives of the research are to examine the Spatio-temporal correlation between LAI of three rice cultivars Sentinel-2A based vegetation indices and to select the most optimum vegetation index in estimating LAI. The field experiment was set up comprising 81 plots, each had a size of 10m x 10 m to resemble a pixel of Sentinel-2A imagery. The results of the analysis show that the vegetation index has a strong correlation with LAI. The Comparison of the four calculated vegetation indices in estimating LAI was performed using a linear regression model and followed by comparing R-squared, RMSE, and Correctness. In general, the EVI2 vegetation index provides the most optimum representation in capturing crop growth patterns based on LAI compared to NDVI, ARVI and SAVI vegetation indices calculated from Sentinel-2A satellite imagery indicated by the better-validated model with the result of RMSE value are 1.12 on V1, 1.11 on V2 and 0.70 on V3. The result of EVI2 Correctness also showed the highest value compared to the other vegetation indices with values of more than 60%, 64.15% on V1, 65.51% on V2, and 78.69% on V3. Further analysis by separating two growth stages could overcome the bias that appears in the LAI data for one life of the crop cycle which is indicated by the decrease of RMSE value on each cultivar planted for both vegetative and generative phases, except for cultivar V3 for the generative phase. The separation of data into two growth stages also increase the percentage value of correctness reaching a number above 60% and there was a value that reached 87%.Pendugaan Indeks Luas Daun (LAI) menjadi penting karena LAI merupakan salah satu parameter dalam analisis model pertumbuhan tanaman. Pertumbuhan tanaman memiliki karakteristik yang berbeda-beda dan sangat dipengaruhi oleh kondisi dan faktor lingkungan. Pertumbuhan tersebut cenderung terjadi dalam waktu yang singkat dan mencakup wilayah yang cukup luas. Oleh karena itu, diperlukan pendekatan untuk menganalisis pola perubahan pertumbuhan tanaman berbasis LAI secara spasial. Penginderaan jauh menawarkan pendekatan yang efektif dan efisien dalam memantau karakteristik pertumbuhan tanaman, yang dapat dilakukan secara time series dengan cakupan wilayah yang cukup luas dengan mendeteksi dan memantau karakteristik fisik tanaman. Parameter yang paling terkenal dan umum digunakan untuk memperkirakan LAI adalah indeks vegetasi yang biasanya dihitung berdasarkan rasio panjang gelombang merah dan NIR, yang dikenal sebagai spectral-signature. Penelitian ini bertujuan untuk mengetahui hubungan spasio-temporal antara LAI pada tiga varietas padi berdasarkan indeks vegetasi Sentinel-2A, dan memilih indeks vegetasi yang paling optimum dalam pendugaan LAI. Dilakukan proses sinkronisasi LAI untuk setiap plot dengan piksel nilai indeks vegetasi berbasis Sentinel-2A. Hasil analisis menunjukkan bahwa indeks vegetasi memiliki korelasi yang kuat dengan LAI. Perbandingan keempat indeks vegetasi yang dihitung dalam pendugaan LAI dilakukan dengan menggunakan model regresi linier dan dilanjutkan dengan membandingkan R-kuadrat, RMSE dan Correctness. Indeks vegetasi EVI2 memberikan representasi paling optimal dalam menangkap pola pertumbuhan tanaman berdasarkan LAI